Paper Citation Record · LEDGER

Autoencoder-Based Hybrid Replay for Class-Incremental Learning

As of 18 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:2505.05926.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2505.05926 v3

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T22:57:32.043271Z

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

28 of 28 outbound references displayed

verified exact2
verified fuzzy8
unresolved18
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation fa24c928-a82c-4b80-99a7-b2b0150d2a84 · outbound

This paper cites On Tiny Episodic Memories in Continual Learning.

Autoencoder-Based Hybrid Replay for Class-Incremental Learning On Tiny Episodic Memories in Continual Learning

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-15T22:57:31.941476Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ab55d288-bfe0-4576-ba0a-9c92d56dc025 · outbound

This paper cites Extended Literature Review Task-based or task-free.

Autoencoder-Based Hybrid Replay for Class-Incremental Learning Extended Literature Review Task-based or task-free

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:57:32.375487Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 45f30027-2ea2-46ba-a528-eb293d5dade2 · outbound

This paper cites Less-forgetting Learning in Deep Neural Networks.

Autoencoder-Based Hybrid Replay for Class-Incremental Learning Less-forgetting Learning in Deep Neural Networks

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-15T22:57:31.958988Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2cc82a98-7421-4cac-a831-8cd0014d095e · outbound

This paper cites Energy-Based Models for Continual Learning.

Autoencoder-Based Hybrid Replay for Class-Incremental Learning Energy-Based Models for Continual Learning

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-08-15T22:57:32.203264Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 612c9c69-5362-4582-8261-c3f62534ac06 · outbound

This paper cites Class-incremental learning: survey and performance evaluation on image classification.

Autoencoder-Based Hybrid Replay for Class-Incremental Learning Class-incremental learning: survey and performance evaluation on image classification

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-15T22:57:31.978909Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fb0c4f70-0328-47e0-8f15-567b2d21cb7b · outbound

This paper cites Federated Class-Incremental Learning: A Hybrid Approach Using Latent Exemplars and Data-Free Techniques to Address Local and Global Forgetting.

Autoencoder-Based Hybrid Replay for Class-Incremental Learning Federated Class-Incremental Learning: A Hybrid Approach Using Latent Exemplars and Data-Free Techniques to Address Local and Global Forgetting

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-15T22:57:31.986915Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d816423d-c537-4906-b0d3-b8dcac6d04a5 · outbound

This paper cites Latent replay for real-time continual learning.

Autoencoder-Based Hybrid Replay for Class-Incremental Learning Latent replay for real-time continual learning

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:57:32.407315Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 54a6cd5f-49cc-4f42-bbf1-b4b5570829a1 · outbound

This paper cites an unresolved cited work.

Autoencoder-Based Hybrid Replay for Class-Incremental Learning Unresolved cited work

Reference 15

Resolution: unresolved; raw_fallback, observed 2026-08-15T22:57:32.396358Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 58f1065e-24e8-4aa4-bf22-2410058e2917 · outbound

This paper cites Incremental Learning of Structured Memory via Closed-Loop Transcription.

Autoencoder-Based Hybrid Replay for Class-Incremental Learning Incremental Learning of Structured Memory via Closed-Loop Transcription

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-15T22:57:31.997713Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f998c6c4-7f9b-41f4-8773-63e568356d3b · outbound

This paper cites S., and King, I.

Autoencoder-Based Hybrid Replay for Class-Incremental Learning S., and King, I

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-15T22:57:32.002278Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3b76231e-b26f-4360-86ea-2874d09d8f98 · outbound

This paper cites Prediction Error-based Classification for Class-Incremental Learning.

Autoencoder-Based Hybrid Replay for Class-Incremental Learning Prediction Error-based Classification for Class-Incremental Learning

Reference 18

Resolution: verified exact; local_arxiv, observed 2026-08-15T22:57:32.090729Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation d7e6edfd-735f-444e-a065-db5476751b16 · outbound

This paper cites A Model or 603 Exemplars: Towards Memory-Efficient Class-Incremental Learning.

Autoencoder-Based Hybrid Replay for Class-Incremental Learning A Model or 603 Exemplars: Towards Memory-Efficient Class-Incremental Learning

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-15T22:57:32.009852Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 06d7edde-b313-49d9-baa3-35cdf5355d30 · outbound

This paper cites an unresolved cited work.

Autoencoder-Based Hybrid Replay for Class-Incremental Learning Unresolved cited work

Reference 20

Resolution: unresolved; raw_fallback, observed 2026-08-15T22:57:32.386093Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 4ddbb242-d107-4618-867f-127ad6d4a6c1 · outbound

This paper cites an unresolved cited work.

Autoencoder-Based Hybrid Replay for Class-Incremental Learning Unresolved cited work

Reference 22

Resolution: unresolved; raw_fallback, observed 2026-08-15T22:57:32.364194Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation d6973b9c-6881-4b99-9b7a-34c4124f0639 · outbound

This paper cites Conversely, in the online scenario, the model visits the data only once as they arrive and cannot iterate on them.

Autoencoder-Based Hybrid Replay for Class-Incremental Learning Conversely, in the online scenario, the model visits the data only once as they arrive and cannot iterate on them

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:57:32.352926Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 5e5a4a04-93e4-4af0-9fea-bddab39a1641 · outbound

This paper cites When learning new tasks, the importance coefficients help in minimizing weight drift.

Autoencoder-Based Hybrid Replay for Class-Incremental Learning When learning new tasks, the importance coefficients help in minimizing weight drift

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:57:32.341209Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 4ddd05e0-a7b0-4358-8158-17ce02ccc1dc · outbound

This paper cites an unresolved cited work.

Autoencoder-Based Hybrid Replay for Class-Incremental Learning Unresolved cited work

Reference 25

Resolution: unresolved; raw_fallback, observed 2026-08-15T22:57:32.330507Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c7d9d4d5-4151-453b-b083-10cb8e500827 · outbound

This paper cites LT often improves the performance when added on top of other strategies (Wu et al., 2019).

Autoencoder-Based Hybrid Replay for Class-Incremental Learning LT often improves the performance when added on top of other strategies (Wu et al., 2019)

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:57:32.319407Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c7c7d9b6-201c-46cc-a2dc-ff76a60b39f8 · outbound

This paper cites an unresolved cited work.

Autoencoder-Based Hybrid Replay for Class-Incremental Learning Unresolved cited work

Reference 28

Resolution: unresolved; raw_fallback, observed 2026-08-15T22:57:32.296709Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 26b3fcd4-2866-43dd-a721-4be408c55dcb · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Autoencoder-Based Hybrid Replay for Class-Incremental Learning Adam: A Method for Stochastic Optimization

Reference 2011

Resolution: unresolved; no resolver link, observed 2026-08-15T22:57:31.967541Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8cff6456-6399-4ad5-a251-285a743cd4a1 · outbound

This paper cites SLDA (Hayes & Kanan,.

Autoencoder-Based Hybrid Replay for Class-Incremental Learning SLDA (Hayes & Kanan,

Reference 2013

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:57:32.308264Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8caa4f3a-071e-4982-a471-a501bbdfb105 · outbound

This paper cites Auto-Encoding Variational Bayes.

Autoencoder-Based Hybrid Replay for Class-Incremental Learning Auto-Encoding Variational Bayes

Reference 2014

Resolution: unresolved; no resolver link, observed 2026-08-15T22:57:31.971290Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 179f1b49-7503-4d7e-92e4-76146c764630 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Autoencoder-Based Hybrid Replay for Class-Incremental Learning Distilling the Knowledge in a Neural Network

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-15T22:57:31.954779Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 158c8ceb-2b61-4770-ad58-4420f176584f · outbound

This paper cites Y ., et al.

Autoencoder-Based Hybrid Replay for Class-Incremental Learning Y ., et al

Reference 2017

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:57:32.418152Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ff236d50-1060-4b2a-a7ef-0942d719a7be · outbound

This paper cites Online Continual Learning with Maximally Interfered Retrieval.

Autoencoder-Based Hybrid Replay for Class-Incremental Learning Online Continual Learning with Maximally Interfered Retrieval

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-15T22:57:31.936834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6426fd54-e21e-45b7-9750-2b9ed92c7aa0 · outbound

This paper cites and Mahmood, A.

Autoencoder-Based Hybrid Replay for Class-Incremental Learning and Mahmood, A

Reference 2019

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:57:32.429065Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 2f8a53e4-5b5b-454b-9e2f-3dffe248a47b · outbound

This paper cites FearNet: Brain-Inspired Model for Incremental Learning.

Autoencoder-Based Hybrid Replay for Class-Incremental Learning FearNet: Brain-Inspired Model for Incremental Learning

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-15T22:57:31.963330Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a1538613-1938-4463-bb59-7f5179f3bf24 · outbound

This paper cites Towards Robust Evaluations of Continual Learning.

Autoencoder-Based Hybrid Replay for Class-Incremental Learning Towards Robust Evaluations of Continual Learning

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-15T22:57:31.949892Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.